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A scalable, explainable machine learning approach for granular-level credit dataset's quality assurance

In: Statistics and beyond: new data for decision making in central banks

Author

Listed:
  • Anak Yodpinyanee
  • Peranut Nimitsurachat
  • Nontawit Cheewaruangroj
  • Supachai Saengthong

Abstract

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Suggested Citation

  • Anak Yodpinyanee & Peranut Nimitsurachat & Nontawit Cheewaruangroj & Supachai Saengthong, 2026. "A scalable, explainable machine learning approach for granular-level credit dataset's quality assurance," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistics and beyond: new data for decision making in central banks, volume 66, Bank for International Settlements.
    • Handle: RePEc:bis:bisifc:66-08
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    File URL: https://www.bis.org/ifc/publ/ifcb66_08.pdf
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    References listed on IDEAS

    as
    1. Davide Nicola Continanza & Andrea del Monaco & Marco di Lucido & Daniele Figoli & Pasquale Maddaloni & Filippo Quarta & Giuseppe Turturiello, 2023. "Stacking machine learning models for anomaly detection: comparing AnaCredit to other banking data sets," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.
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